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See how Couchbase outperforms MongoDB in the NoSQL technical comparison report

Critical MongoDB disadvantages and how Couchbase overcomes them

Challenge 1:
Elastic capacity and scale-agnostic architecture

MongoDB leaves data at risk because it’s inflexible and can’t adapt to modern workloads 

Learn more about MongoDB’s architectural weaknesses >


MongoDB’s performance suffers from master-slave replication, which can cause idle nodes and low hardware utilization.
All MongoDB services share the same resources, making it impossible to isolate workloads.
Single-node and sharded environments require different development patterns and changes to the way applications function. This discourages the use of sharding by putting the burden on the application and operations teams to know their data and tune it differently for different access patterns.


Couchbase was designed as a masterless, clustered, and replicated distributed database from day one – all nodes can read and write. All users benefit from clustering and rebalance, which improves data safety.
Multi-Dimensional Scaling (MDS) enables independent scaling and complete workload isolation.
With high performance at scale, application behavior is unchanged whether growing from a single dev node to 3 test nodes or to 20 production nodes in a cloud.

Challenge 2:
Performance at scale

MongoDB performance rapidly degrades as the cluster size or number of users increases

See how Couchbase outperforms MongoDB on enterprise workloads >



MongoDB's performance suffers from limited page cache capability, requiring third-party tools to achieve acceptable norms for an enterprise-scale database.

Another MongoDB disadvantage is that inefficient routing layers add complexity and latency to requests.

Also, rigid data and index partitioning limitations require slow cross-node scatter-gather methods for some queries. This approach slows query performance, especially on enterprise clusters.


Couchbase has a tightly managed and fully integrated caching layer for both data and indexes, and doesn’t need additional caching products for high performance.

Intelligent routing with direct application-to-node document lookup allows for efficient communications with single network hop access.

Global indexes allow indexes to be partitioned independently of data, minimizing latency for important queries.

Challenge 3:
Integrated enterprise functionality

MongoDB limits many features depending on deployment model or third-party requirements

Learn more about integrated Couchbase capabilities >


MongoDB’s key disadvantage is that some of its most attractive capabilities are bolted on rather than tightly coupled to the core database. These bolt-ons include replication, sharding, limited full-text search, and SQL-based analytics.

Because MongoDB uses a unique proprietary query language that can be hard to learn (Figure 1), you must hire developers with specific platform experience in order to get lasting value.

Finally, sharded environments have functionality limitations. Stitch functions and mobile sync are available only through the hosted Atlas offering.


Couchbase has unified application and management APIs across document lookup, structured query, full-text search, analytics, and triggers. This gives developers easy access to all functionality, including security and management capabilities.

Couchbase’s query language extends ANSI SQL and leverages traditional database skills for easier adoption (Figure 1).

All functionality is available across all deployment models: on premises, containers, and cloud.

MongoDB Query
Couchbase N1QL
        { "$match": {
                "$and": [
                    {"symbol": {
                            "$in": [
                    { "value": {
                            "$gt": 0 }}]}},
        { "$group": {
                "_id": {
                    "symbol": "$symbol" },
                "sum(value * volume)": {
                    "$sum": {
                        "$multiply": [
        { "$project": {
                "_id": 0,
                "sum(value * volume)": "$sum(value * volume)",
                "symbol": "$_id.symbol"}}
        { "$sort": {
                "sum(value * volume)": -1,
                "symbol": 1 }}]})
SELECT SUM(value * volume) AS val, symbol
FROM   db.stocks
WHERE  symbol IN ( "AAPL", "GOOG" ) AND value > 0
GROUP  BY symbol
ORDER  BY val DESC, symbol ASC
Figure 1: Couchbase extends ANSI SQL to support the flexible schema of JSON

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